In Internet datacenters (IDC), heat generated by equipment such as computers need to be removed so that the equipment may operate properly. In a typical IDC, a centralized source provides the cooling air. The cooling air is distributed by fans, false floors, and the like to the equipment including servers, storage devices, power supply units, and the like.
Conventionally, the practice of designing a cooling system for an IDC is to perform a simple energy balance and to employ some “rule of thumb” approach. Energy balance requires a calculation of the expected heat generation and using the calculation to specify the capacity of the cooling system. Then, rule of thumb approaches are used to actually lay out the IDC including placements of the equipment, vent distribution for the cooling air, exhaust, redundancy, and the like. In the conventional design approach, temperature and velocity gradients (differences in heat generation, dissipation, cooling air delivery, and the like) are accounted for by designing in excess cooling capacity (a brute force approach).
However, the amount of heat generated by equipment has been steadily increasing. For example, in 1992, an average heat load per machine area in servers was between 200–300 watts per square foot (W/ft2). The heat load is expected to exceed 2,500 W/ft2 in a relatively short period of time. The conventional brute force approach to IDC cooling layout design will be insufficient in the future, and will be prohibitively expensive.
Computational large-scale modeling methods are available to model the behavior of a layout design for the IDC. However, to analyze the layout with the large-scale modeling, an extensive numerical simulation is performed that involves solving non-linear equations. Such a simulation take a very long time to run. For example, a simulation of an IDC with 32,000 square feet may take over 18 hours. To optimize the layout for efficient cooling, multiple simulations are required. However, because each simulation takes a long time to run, optimization using large-scale modeling is prohibitively expensive.
However, a lack of cooling optimization leads to wasted energy, which directly translates into higher operation and maintenance costs. In addition, without optimization, it is difficult, if not impossible, to plan for disaster situation such as a failure of some global air-conditioner, for example.